Time series data storage using Redis and Mongo
Project description
- Version:
- 0.5.0
- Download:
- Source:
- Keywords:
python, redis, mongo, time, timeseries, rrd, gevent, statistics
Overview
Kairos provides time series storage using Redis or Mongo backends. Kairos is intended to replace RRD and Whisper in situations where the scale and flexibility of Redis or Mongo is required. It works with gevent out of the box.
Recommended for python 2.7 and later, it can work with previous versions if you install OrderedDict.
Usage
Kairos supports redis and mongo storage using the same API.
Redis
from kairos import Timeseries
import redis
client = redis.Redis('localhost', 6379)
t = Timeseries(client, type='histogram', read_func=int, intervals={
'minute':{
'step':60, # 60 seconds
'steps':120, # last 2 hours
}
})
t.insert('example', 3.14159)
t.insert('example', 2.71828)
print t.get('example', 'minute')
Mongo
from kairos import Timeseries
import pymongo
client = pymongo.MongoClient('localhost')
t = Timeseries(client, type='histogram', read_func=int, intervals={
'minute':{
'step':60, # 60 seconds
'steps':120, # last 2 hours
}
})
t.insert('example', 3.14159)
t.insert('example', 2.71828)
print t.get('example', 'minute')
Each Timeseries will store data according to one of the supported types. The keyword arguments to the constructor are:
type
One of (series, histogram, count). Optional, defaults to "series".
series - each interval will append values to a list
histogram - each interval will track count of unique values
count - each interval will maintain a single counter
gauge - each interval will store the most recent data point
prefix
Optional, Redis only, is a prefix for all keys in this timeseries. If
supplied and it doesn't end with ":", it will be automatically appended.
read_func
Optional, is a function applied to all values read back from the
database. Without it, values will be strings for Redis, whatever
`write_func` defined for Mongo. Must accept a string value for Redis
(empty string for no data) and can return anything.
write_func
Optional, is a function applied to all values when writing. Can be
used for histogram resolution, converting an object into an id, etc.
Must accept whatever can be inserted into a timeseries and return an
object which can be saved according to the rules of Redis or Mongo.
intervals
Required, a dictionary of interval configurations in the form of:
{
# interval name, used in Redis and Mongo keys and should conform to best
# practices according to the backend type.
minute: {
# Required. The number of seconds that the interval will cover,
# or a supported Gregorian interval.
step: 60,
# Optional. The maximum number of intervals to maintain. If supplied,
# will use Redis and Mongo expiration to delete old intervals, else
# intervals exist in perpetuity.
steps: 240,
# Optional. Defines the resolution of the data, i.e. the number of
# seconds in which data is assumed to have occurred "at the same time".
# So if you're tracking a month-long time series, you may only need
# resolution down to the day, or resolution=86400. Defaults to same
# value as "step". Can also be a Gregorian interval.
resolution: 60,
}
}
In addition to specifying step and resolution in terms of seconds, kairos also supports a simplified format for larger time intervals. For hours (h), days (d), weeks (w), months (m) and years (y), you can use the format 30d to represent 30 days, for example.
As of 0.3.0, kairos also supports the Gregorian calendar for step and resolution. Either or both parameters can use the terms [daily, weekly, monthly, yearly] to describe an interval. You can also mix these terms between step and resolution (e.g. daily in 1h resolutions). The expiration time for Gregorian dates is still defined in terms of seconds and may not match the varying month lengths, leap years, etc. Gregorian dates are translated into strptime- and strftime-compatible keys are so may be easier to use in raw form or any integrated tools.
Each retrieval function will by default return an ordered dictionary, though condensed results are also available. Run script/example to see standard output; watch -n 4 script/example is a useful tool as well.
Inserting Data
There is one method to insert data, Timeseries.insert which takes the followng arguments:
name The name of the statistic
value The value of the statistic (optional for count timeseries)
timestamp (optional) The timestamp of the statistic, defaults to time.time() if not supplied
For series and histogram timeseries types, value can be whatever you’d like, optionally processed through the write_func method before being written to storage. Depending on your needs, value (or the output of write_func) does not have to be a number, and can be used to track such things as unique occurances of a string or references to other objects, such as MongoDB ObjectIds. Note that many of the aggregate functions in histogram expect the data to be real numbers.
For the count type, value is optional and should be a float or integer representing the amount by which to increment or decrement name; it defaults to 1.
For the gauge type, value can be anything and it will be stored as-is.
Data for all timeseries is stored in “buckets”, where any Unix timestamp will resolve to a consistent bucket name according to the step and resolution attributes of a schema. A bucket will contain the following data structures for the corresponding series type.
series list
histogram dictionary (map)
count integer or float
gauge value
Reading Data
There are two methods to read data, Timeseries.get and Timeseries.series. get will return data from a single bucket, and series will return data from several buckets.
get
Supports the following parameters:
name The name of the statistic, or a list of names whose data will be joined together.
interval The named interval to read from
timestamp (optional) The timestamp to read, defaults to time.time()
condensed (optional) If using resolutions, True will collapse the resolution data into a single row
transform (optional) Optionally process each row of data. Supports [mean, count, min, max, sum], or any callable that accepts datapoints according to the type of series (e.g histograms are dictionaries, counts are integers, etc). Transforms are called after read_func has cast the data type and after resolution data is optionally condensed. If transform is one of (list,tuple,set), will load the data once and run all the transforms on that data set. If transform is a dict of the form { transform_name : transform_func }, will run all of the transform functions on the data set.
fetch (optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads.
Returns a dictionary of { timestamp : data }, where timestamp is a Unix timestamp and data is a data structure corresponding to the type of series, or whatever transform returns. If not using resolutions or condensed=True, the length of the dictionary is 1, else it will be the number of resolution buckets within the interval that contained data. If transform is a list, data will be a dictionary of { transform_func : transformed_data }. If transform is a dict, data will be a dictionary of { transform_name : transformed_data }.
series
Almost identical to get, supports the following parameters:
name The name of the statistic, or a list of names whose data will be joined together.
interval The named interval to read from
start (optional) The timestamp which should be in the first interval of the returned data.
end (optional) The timestamp which should be in the last interval of the returned data.
steps (optional) The number of steps in the interval to read, defaults to either steps in the configuration or 1. Ignored if both start and end are defined. If either start or end are defined, steps is inclusive of whatever interval that timestamp falls into.
condensed (optional) If using resolutions, True will collapse the resolution data into a single row
transform (optional) Optionally process each row of data. Supports [mean, count, min, max, sum], or any callable that accepts a list of datapoints according to the type of series (e.g histograms are dictionaries, counts are integers, etc). Transforms are called after read_func has cast the data type and after resolution data is optionally condensed. If transform is one of (list,tuple,set), will load the data once and run all the transforms on that data set. If transform is a dict of the form { transform_name : transform_func }, will run all of the transform functions on the data set.
collapse (optional) Optionally collapse all of the data in the date range into a single result.
fetch (optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads.
Returns an ordered dictionary of { interval_timestamp : { resolution_timestamp: data } }, where interval_timestamp and resolution_timestamp are Unix timestamps and data is a data structure corresponding to the type of series, or whatever transform returns. If not using resolutions or condensed=True, the dictionary will be of the form { interval_timestamp : data }.
All variations of transform and the resulting format of data are the same as in get.
If both start and end are defined, the returned data will start and end on intervals including those timestamps. If only start is defined, then the return data will start with an interval that includes that timestamp, with the total number of intervals returned defined by steps. If only end is defined, then the return data will end with an interval that includes that timestamp, with the total number of intervals preceeding it defined by steps.
It is important to note that the interval timestamps in the returned data will not necessarily match start or end. This is because of the consistent hashing scheme that kairos uses, such that start and end will be translated into the bucket in which it can be found.
Customized Reads
ALPHA This feature is still being explored and the API may change significantly.
There are times when the data in a timeseries requires processing to be pushed onto the datastore. As there is no good way to do this generically, the get and series API support the keyword argument fetch which should be a callable. The arguments and expected return type will depend on the datastore and the type of the timeseries.
IMPORTANT You are welcome to change the type of the return value, but be wary that transforms, condense and collapse functionality may not work properly with the changed data types.
Redis
The function must be in the form of fetch(handle, key), where:
handle Either a Redis client or pipeline instance
key The key for the timeseries data
The return value should correspond to the data type of timeseries, e.g. dict for a histogram.
Mongo
The function must be in the form of fetch(handle, **kwargs), where:
handle A PyMongo Collection
spec The (suggested) query specification
sort The (suggested) sort definition for the query
method The suggested method to use on the handle
The required return value depends on the value of method.
find_one Should return a hash in the form { value : <data> }, where <data> should correspond to the data type of the timeseries, e.g. list for a series. May directly return a result from pymongo.collection.find_one.
find Should return an iterable in the form [ { value: <data> }, ... ], where <data> follows the same rules as find_one.
Deleting Data
To delete the data, call Timeseries.delete with the name of your statistic, and all values in all intervals will be deleted.
Dragons!
Kairos achieves its efficiency by using Redis or Mongo TTLs and data structures in combination with a key naming scheme that generates consistent keys based on any timestamp relative to epoch. However, just like RRDtool, changing any attribute of the timeseries means that new data will be stored differently than old data. For this reason it’s best to completely delete all data in an old time series before creating or querying using a new configuration.
Installation
Kairos is available on pypi and can be installed using pip
pip install kairos
If installing from source:
with development requirements (e.g. testing frameworks)
pip install -r development.pip
without development requirements
pip install -r requirements.pip
Note that kairos does not by default require the redis package, nor does it require hiredis though it is strongly recommended.
Tests
Use nose to run the test suite.
$ nosetests
Roadmap
Round-robbin intervals for datastores without TTLs
Round-robbin databases: memcache (and compatible, e.g. ElastiCache), Riak, DynamoDB, SimpleDB, GDBM, Berkeley DB, and more
Redis optimizations
Capped collection support for mongo
Expose the native commands for various data stores (e.g. “sismember”) for single interval and series queries.
Bloom filters
“Native” transforms that leverage data store features (e.g. “length”)
Joined series populate a data structure at query time
Joined series support concurrency “runner”
License
This software is licensed under the New BSD License. See the LICENSE.txt file in the top distribution directory for the full license text.
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